Traditionally, driving environments are described using traditional maps. These maps provide a geographical representation derived from sensors on a well-equipped survey vehicle. The features that are detected by these vehicles are generally visual, such as paint markings detected by cameras, and spatial, curbs and buildings detected by cameras or LIDAR. In all cases the features are static. The features represented in the map are available to a single survey vehicle when it passes and they are generally stable on timescales of years corresponding to the time needed to create, distribute and use the resulting map. Features such as average speed or the distribution of speeds are not available to the survey vehicles and hence are not intrinsically part of these maps. Speed attributes may be later added to a map based on observations from vehicles or speed limit signs, but these are used for navigation and planning purposes and are only available at the link level (that is the time to traverse a section of road connecting two intersections).
In the past, practitioners have attempted to derive driver behavior models and predict driver patterns (speed, position, acceleration, etc.) based on a map (as described above), sensor data available in the vehicle (radar, vision system, etc.), and/or sensors external to a vehicle (hereafter identified as “external observations”). These models are often used in traffic simulations to describe the motion of simulated vehicles (based on traditional map data), for the evaluation of new vehicle sensors and systems in simulated environments, and for predicting a future vehicle state, as well as other purposes.
For example, curve speed warning systems provide a warning to a driver if he or she is entering a curve at a speed that is judged to be unsafe. In the simplest case, the speed is judged unsafe if the lateral acceleration is, or is expected to be in some short time interval, above some threshold, say 0.4 g, this lateral acceleration is derived from the curvature of the road and the (expected) speed of the vehicle by the relation a=v/r, where r is the radius of curvature of the road, a is the acceleration, and v is the speed This approach works well in many cases, however, there are cases where this method results in erroneous warnings of excessive speed, which, if low, may result in annoyance to the driver, or, if the computed speed is too high, a lack of warning when warning is appropriate. The errors can result from one of several mechanisms. One reason for calculating an incorrect speed is errors in the map. Curvature can be very sensitive to small errors in position for the road centerline, especially for very tight curves, thus even small relative errors in position between points in the map may result in significant errors in the maximum speed. The second reason for errors is that the road may be banked in such a way as to allow a higher or lower speed. Often bank angle of the road is not included in the map data. A third reason for errors is compound curves were there may be several different curvatures in close succession making it very difficult for the algorithm to properly compute an optimum speed. Errors may also be caused because drivers do not follow the centerline geometry e.g. they cut the curve resulting in a lower radius of curvature than that in the database. Humans may select a higher or lower speed due to other considerations as well, such as the width of the shoulder, the surface of the road, the ability to see the full extent of a curve, etc. None of these factors are included in the traditional map database. Additionally some drivers are better than others, or at least drive faster, thus a proper speed alert for one driver is an annoyance alarm for another.
Additionally, existing models have limited fidelity, due in part to the complexity of human driving behaviors, but also to the existence of many factors a human takes into consideration that are not available to sensors and algorithms used for traditional driver behavior modeling, such as road surface, weather conditions, time of day, vehicle sightlines, etc. The fidelity of existing behavior models are limited by the content available in existing map databases.